Context-adaptive RSSI-based misbehavior detection

ABSTRACT

Aspects relating to abnormal transmission identification include, for example, a method involving, at a receiving device, receiving a plurality of V2X messages from a plurality of transmitting devices, obtaining a plurality of RSSI-to-distance data pairs, including an RSSI-to-distance data pair for each of the plurality of V2X messages, establishing an RSSI-to-distance relationship model based, at least in part, on the plurality of RSSI-to-distance data pairs. The method further involves, at the receiving device, receiving an additional V2X message from a transmitting device different from the plurality of transmitting devices, obtaining an additional RSSI-to-distance data pair for the additional V2X message, and comparing the additional RSSI-to-distance data pair to the RSSI-to-distance relationship model. The method further involves, in response to determining that the additional RSSI-to-distance data pair fails a criterion for conforming to the RSSI-to-distance relationship model, identifying the additional V2X message as an abnormal transmission.

BACKGROUND

Various aspects of the disclosure relate to misbehavior detection in thecontext of vehicle-to-everything (V2X) communications. V2X is broad termdescribing a class of technology that allows a vehicle to communicatewith other entities, and vice versa. V2X may encompass specific types ofcommunication such as vehicle-to-infrastructure (V2I),vehicle-to-network (V2N), vehicle-to-vehicle (V2V),vehicle-to-pedestrian (V2P), vehicle-to-device (V2D) and vehicle-to-grid(V2G). By enabling vehicles and infrastructure to share information suchas vehicular type, speed, location, trajectory, traffic signal status,timing, etc., V2X can significantly improve road safety and trafficefficiency. However, V2X systems can be vulnerable to misbehavior suchas location spoofing, which can cause artificial traffic congestion andpose other safety hazards. Misbehavior detection based solely onapplication layer data can often be circumvented by software algorithms.There is a significant need for improved techniques for misbehaviordetection in V2X communications.

BRIEF SUMMARY

Aspects relating to abnormal transmission identification are disclosed.In one example, a method involves, at a receiving device, receiving aplurality of V2X messages from a plurality of transmitting devices. Themethod further involves obtaining a plurality of RSSI-to-distance datapairs, including an RSSI-to-distance data pair for each of the pluralityof V2X messages. The method further involves establishing anRSSI-to-distance relationship model based, at least in part, on theplurality of RSSI-to-distance data pairs. The method further involves,at the receiving device, receiving an additional V2X message from atransmitting device different from the plurality of transmittingdevices. The method further involves obtaining an additionalRSSI-to-distance data pair for the additional V2X message. The methodfurther involves comparing the additional RSSI-to-distance data pair tothe RSSI-to-distance relationship model. The method further involves, inresponse to determining that the additional RSSI-to-distance data pairfails a criterion for conforming to the RSSI-to-distance relationshipmodel, identifying the additional V2X message as an abnormaltransmission.

In another example, a method involves, at a receiving device, receivinga plurality of V2X messages from a plurality of transmitting devices.The plurality of V2X messages may correspond to a plurality ofRSSI-to-distance data pairs, including an RSSI-to-distance data pair foreach of the plurality of V2X messages. The plurality of RSSI-to-distancedata pairs may correspond to an RSSI-to-distance relationship model. Themethod further involves, at the receiving device, receiving anadditional V2X message from a transmitting device different from theplurality of transmitting devices. The additional V2X messagecorresponds to an additional RSSI-to-distance data pair, and theadditional RSSI-to-distance data pair fails a criterion for conformingto the RSSI-to-distance relationship model. The method further involves,at the receiving device, identifying the additional V2X message as anabnormal transmission.

In another example, an apparatus includes a receive radio unitconfigured to receive a plurality of V2X messages from a plurality oftransmitting devices and receive an additional V2X message from atransmitting device different from the plurality of transmittingdevices. The apparatus further includes one or more processorsconfigured to obtain a plurality of RSSI-to-distance data pairs,including an RSSI-to-distance data pair for each of the plurality of V2Xmessages. The one or more processors are further configured to establishan RSSI-to-distance relationship model based, at least in part, on theplurality of RSSI-to-distance data pairs, obtain an additionalRSSI-to-distance data pair for the additional V2X message, and comparethe additional RSSI-to-distance data pair to the RSSI-to-distancerelationship model. The one or more processors are further configuredto, in response to determining that the additional RSSI-to-distance datapair fails a criterion for conforming to the RSSI-to-distancerelationship model, identify the additional V2X message as an abnormaltransmission.

In yet another example, a system includes means for, at a receivingdevice, receiving a plurality of V2X messages from a plurality oftransmitting devices. The system further includes means for obtaining aplurality of RSSI-to-distance data pairs, including an RSSI-to-distancedata pair for each of the plurality of V2X messages. The system furtherincludes means for establishing an RSSI-to-distance relationship modelbased, at least in part, on the plurality of RSSI-to-distance datapairs. The system further includes means for, at the receiving device,receiving an additional V2X message from a transmitting device differentfrom the plurality of transmitting devices. The system further includesmeans for obtaining an additional RSSI-to-distance data pair for theadditional V2X message. The system further includes means for comparingthe additional RSSI-to-distance data pair to the RSSI-to-distancerelationship model. The system further includes means for, in responseto determining that the additional RSSI-to-distance data pair fails acriterion for conforming to the RSSI-to-distance relationship model,identifying the additional V2X message as an abnormal transmission.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of the disclosure are illustrated by way of example. In theaccompanying figures, like reference numbers indicate similar elements.

FIG. 1 shows an example of a V2X environment that may incorporate one ormore embodiments of the present disclosure.

FIG. 2 presents an illustrative format of a Basic Safety Message (BSM),which is an example a type of V2X communication that may be sent betweenentities in environment and used by a receiving device for abnormaltransmission identification, according to one embodiment of thedisclosure.

FIG. 3 presents a plot of receive signal strength indicator (RSSI)values versus distance and how such a plot might be used foridentification of abnormal V2X transmissions.

FIG. 4 is a block diagram of illustrative components within acommunications device configured to identify abnormal V2X transmissionsin the face of aforementioned challenges, according to an embodiment ofthe disclosure.

FIG. 5 is a block diagram illustrating operations of an abnormaltransmission detector, according to an embodiment of the disclosure.

FIG. 6 presents a plurality of predetermined RSSI-to-distancerelationship models, according to one embodiment of the disclosure.

FIG. 7 shows the results of a chi-square test performed to fit a firstset of RSSI-to-distance observations to each of seven predeterminedRSSI-to-distance relationship model shown in FIG. 6.

FIG. 8 shows the results of a chi-square test performed to fit a secondset of RSSI-to-distance observations to each of seven predeterminedRSSI-to-distance relationship model shown in FIG. 6.

FIG. 9A illustrates an unobstructed scenario 900 for V2X signalpropagation between two devices.

FIG. 9B illustrates an obstructed scenario 910 for V2X signalpropagation between two devices.

FIG. 10 is an example of a log file 1000 generated by a host vehiclelisting details associated with BSM messages that have been identifiedas abnormal transmissions.

FIG. 11 is a flowchart illustrating a process 1100 for abnormaltransmission identification, according to an embodiment of thedisclosure.

FIG. 12 is a block diagram of various hardware and software componentsof a vehicle, according to an embodiment.

DETAILED DESCRIPTION

Several illustrative embodiments will now be described with respect tothe accompanying drawings, which form a part hereof. While particularembodiments, in which one or more aspects of the disclosure may beimplemented, are described below, other embodiments may be used andvarious modifications may be made without departing from the scope ofthe disclosure or the spirit of the appended claims.

FIG. 1 shows an example of a V2X environment 100 that may incorporateone or more embodiments of the present disclosure. As shown, environment100 may comprise a number of different entities, including vehicles 102and 104, pedestrians 106, 108, and 110, and infrastructure components112 (signal light), 114 (signal light), 116 (signal light), and 118(crosswalk sensor). These entities may engage in V2X communications withone another to implement improved road safety and traffic efficiency. Areceiving device receiving V2X communications, such as any of vehicles102 and 104, pedestrians 106, 108, and 110 (e.g., mobile devices carriedby the pedestrians), or infrastructure components 112, 114, 116, and118, may determine that a particular V2X communication received is anabnormal transmission and thereby conclude that the transmitting deviceis misbehaving. Such a technique for abnormal transmissionidentification may involve illustrative steps described below, accordingto various embodiments of the disclosure.

As described herein, a “receiving device” may refer to a communicationsdevice capable of bi-directional communications but whose receptioncapabilities are being emphasized for purposes of discussion. Areceiving device may, but is not required to be, actively performing areception operation. For example, a receiving device may have performedreception previously, will be performing reception at a later time, mayconditionally perform reception based on a condition that may or may notoccur, etc. Similarly, a “transmitting device” may refer to acommunications device capable of bi-direction communications but whosetransmission capabilities are being emphasized for purposes ofdiscussion. Likewise, a transmitting device may, but is not required tobe, actively performing a transmission operation. For example, atransmitting device may have performed transmission previously, will beperforming transmission at a later time, may conditionally performtransmission based on a condition that may or may not occur, etc.

For example, the technique may comprise, at the receiving device,receiving a plurality of first V2X messages from a plurality of firsttransmitting devices. For example, the receiving device may be vehicle102. The first plurality of V2X messages from the plurality of firsttransmitting devices may include, for instance, a V2X message sent frompedestrian 110, a V2X message sent from vehicle 104, and a V2X messagesent from infrastructure component 112. The technique may furthercomprise, for each first V2X message of the plurality of first V2Xmessages, obtaining an RSSI-to-distance data pair comprising (1) areceived signal strength indicator (RSSI) value and (2) a distancemeasure associated with an estimated distance between the receivingdevice and a corresponding first transmitting device of the first V2Xmessage, to form a plurality of RSSI-to-distance data pairs for theplurality of first V2X messages. The technique may further comprise,based on the plurality of RSSI-to-distance data pairs for the pluralityof first V2X messages, selecting a predetermined RSSI-to-distancerelationship model from a plurality of predetermined RSSI-to-distancerelationship models. The technique may further comprise, at thereceiving device, receiving a second V2X message from a secondtransmitting device different from the first plurality of transmittingdevices. For example, the second V2X message from the secondtransmitting device may be a V2X message sent from pedestrian 108. Thetechnique may further comprise obtaining an RSSI-to-distance data pairfor the second V2X message comprising (1) an RSSI value and (2) adistance measure associated with an estimated distance between thereceiving device and the second transmitting device. The technique mayfurther comprise determining that the RSSI-to-distance data pair for thesecond V2X message fails a criterion for conforming to the selected,predetermined RSSI-to-distance relationship model. Finally, thetechnique may comprise identifying the second V2X message as an abnormaltransmission based at least in part on the determining that theRSSI-to-distance data pair for the second V2X message fails thecriterion for conforming to the selected, predetermined RSSI-to-distancerelationship model. In this manner, vehicle 102 may determine that theV2X message received from pedestrian 108 is an abnormal transmission,which may be deemed to be misbehavior within the V2X environment.

FIG. 2 presents an illustrative format of a Basic Safety Message (BSM),which is an example a type of V2X communication that may be sent betweenentities in environment 100 and used by a receiving device for abnormaltransmission identification, according to one embodiment of thedisclosure. A BSM message, whose use is in V2X communications isprevalent in locations such as the United States, is described here forillustrative purposes. Other types of messages that may be used caninclude, for example, Cooperative Awareness Message (CAM) orDecentralized Environmental Notification Message (DENM), whose use inV2X communications is prevalent in locations such as Europe. The use ofRSSI values and location data contained within V2X communications formisbehavior detection in accordance with embodiments of the presentdisclosure is applicable for BSM, CAM, and/or DENM messages. Referringback to the example shown in FIG. 2, the BSM format shown includes datato identify the message and specify the size and type of the message.The BSM format shown also includes data on the location/position of theentity. In this case, location data includes, for example, latitude,longitude, and elevation data. In addition, the BSM format can alsoinclude information such as vehicle heading, steering wheel angle,acceleration and yaw rate, brake system status, vehicle size, etc.

Misbehavior within an V2X environment can take the form of BSM messagessent with inaccurate information designed to disrupt traffic, createcongestion, or cause some other disruption. For example, a misbehavingentity might send a large number of BSM messages that appear to comefrom different vehicles (e.g., with different temporary identifiers(“TemporaryIDs”). These BSM messages may contain location and trajectorydata indicating a large number of nearby vehicles are all headed towarda particular intersection, when in fact such vehicles do not exist. Thesender of these “fake” BSM messages might be a mobile phone on a singleuser, e.g., pedestrian 108 in FIG. 1. The sender might employ softwarealgorithms to overcome any V2X misbehavior detection systems basedsolely on application layer data, by employing software algorithms tospoof temporary identifiers, mimic vehicle trajectory data to conform toreal-world map coordinates for existing roadways, etc. Thus, themisbehavior being perpetrated might be difficult to detect. According toembodiments of the disclosure, a receiving device may obtain anRSSI-to-distance data pair associated with reception of a particular BSMmessage, determine whether the RSSI-to-distance data pair passes orfails a criterion for conforming to an expected RSSI-to-distancerelationship model, and use such information to conclude whether the V2Xcommunication comprising the BSM message is an abnormal transmission.

FIG. 3 presents a plot of receive signal strength indicator (RSSI)values versus distance and how such a plot might be used foridentification of abnormal V2X transmissions. The y-axis represents theRSSI value in logarithmic form, expressed in decibels (dB). The x-axisrepresents distance expressed in meters. According to embodiments of thepresent disclosure, the device receiving a BSM message may scrutinizethe RSSI value associated with reception of the BSM message and thedistance between the sender and the device receiving the BSM message, inorder to determine whether the BSM message ought to be identified as anabnormal transmission. As discussed previously with reference to FIG. 2,a BSM message typically includes information pertaining to the locationof the sender (e.g., vehicle, pedestrian, infrastructure, etc.) of theBSM message. The sender's location can thus be obtained at the devicereceiving the BSM message, by parsing the BSM message. The devicereceiving the BSM message also knows its own location. With knowledge ofboth locations, the device receiving the BSM message can compute thedistance between the sender and the receiving device. At the same time,the device receiving the BSM message can obtain an RSSI value associatedwith reception of the BSM message (e.g., from the receive radio in thephysical layer). The device receiving the BSM message can then evaluatewhether the (1) RSSI value associated with reception of the BSM messageand (2) computed distance between the sender and the device receivingthe BSM message conform to, or fail to conform to, an expectedRSSI-to-distance relationship model.

In FIG. 3, curves 302, 304, and 306 represent various attributes of aparticular RSSI-to-distance relationship model. These curves may bederived from a distribution of RSSI values that a device receiving a BSMmessage, for example, might expect to see as a function of distancebetween the sender and the device receiving the BSM message. Again, aBSM message is described here for illustrative purposes. Other types ofmessages, including a CAM message or a DENM message, may also be used.Returning to FIG. 3, curve 302 represents the mean RSSI value, curve 304represents the maximum RSSI value, and curve 306 represents the minimumRSSI value. Here, the “maximum” and “minimum” RSSI values may representupper and lower thresholds that have been set to establish a range inwhich RSSI values may be deemed to be acceptable or normal. RSSI valuesfalling outside such a range maybe deemed to be abnormal, e.g., deemedto be sent from a misbehaving sender. Thus, curve 304 (maximum) andcurve 306 (minimum) represent an example of a criterion that has beenestablished to determine whether a received V2X message conforms to theRSSI-to-distance relationship model. As can be seen, the expected RSSIvalue (e.g., curves 302, 304, and 306) generally decreases as thedistance between the sender and the receiving device grows. However, theexpected RSSI value may not be a strictly decreasing function ofdistance—i.e. there may be certain intervals in which the expected RSSIvalue increases with distance. The shape of the RSSI-to-distancerelationship can vary depending on the environment and signal pathsinvolved, as discussed in more detail in later sections.

The model for an RSSI-to-distance relationship can range from simple tocomplex, and it can be expressed in different ways. In the example shownin FIG. 3, a mean curve 302, a maximum curve 304, and a minimum curve304 are shown. However, measures other than mean, maximum, and minimumcan be used. For example, an RSSI-to-distance relationship model can beexpressed in terms of a probability distribution function at eachdistance value. Each probability distribution function may comprise aplurality of frequency bins. Different bin sizes may be implemented.Indeed, measures such as the mean, maximum, and minimum may be derivedfrom the distribution of RSSI values for each distance value. Thecriterion for evaluating model conformance may be based on such aprobability distribution but take forms other than a maximum curve andminimum curve. In addition, while curves 302, 304, and 306 in FIG. 3appear to reflect data points that are densely spaced over distance, theRSSI-to-relationship model can be realized using data points that aresparsely spaced over distance. Interpolation and smoothing can beapplied to “fill in” values between sparsely spaced data points.Finally, data underlying the RSSI-to-distance relationship model may bederived from real-world observations, simulations, calculations, and/orother sources.

Sample points 308, 310, and 312 satisfy a criterion for conforming tothe RSSI-to-distance relationship model presented in FIG. 3. Differentcriteria may be established. Here, the criterion comprises a simplerequirement that at the particular distance value associated with thesample point, the observed RSSI value must fall within a rangeestablished by the maximum RSSI value curve 304 and the minimum curve306. For example, sample point 308 corresponds to a BSM message thatappears to be a normal V2X transmission. Sample point 308 is shown tohave an observed RSSI value of about −19 decibel-milliwatts (dBm) and adistance value of about 300 meters. At the distance value of 300 meters,the maximum expected RSSI value according to curve 304 is about −17.9dBm, and the minimum expected RSSI value according to curve 306 is about−20 dBm. The observed RSSI value of −19 dBm falls within the rangeestablished by the minimum and maximum values, and sample point 308 isdeemed to satisfy the criterion for conforming to the RSSI-to-distancerelationship model. In a similar manner, sample points 310 and 312 canalso be shown to satisfy the criterion for conforming to theRSSI-to-distance relationship model.

By contrast, sample points 314 and 316 fail to satisfy the samecriterion for conforming to the RSSI-to-distance relationship modelpresented in FIG. 3. For example, sample point 314 corresponds to a BSMmessage that appears to be an abnormal V2X transmission. Sample point314 is shown to have an observed RSSI value of about −13.6 dBm and adistance value of about 540 meters. At the distance of 540 meters, themaximum expected RSI value according to curve 304 is about −19.5 dBm,and the minimum expected RSSI value according to curve 36 is about −21.6dBm. Thus, the observed RSSI value of −13.6 dBm falls outside the rangeestablished by the maximum and minimum values, and sample point 314 isdeemed to not satisfy the criterion for conforming to theRSSI-to-distance relationship model. In a similar manner, sample point316 can be shown to fail to satisfy the criterion for conforming to theRSSI-to-distance relationship model.

FIG. 3 demonstrates that, once an RSSI-to-distance relationship model isknown, it can be used to effectively discriminate between normal andabnormal V2X transmissions. Detecting one or more abnormal transmissionsmay lead to a conclusion that the sender is misbehaving. However, theV2X environment may not be easily predictable. Indeed, the V2Xenvironment may be dynamic in nature, with entities located in differentplaces and moving in different trajectories. All of this makes itdifficult to ascertain a useful RSSI-to-distance relationship model.

FIG. 4 is a block diagram of illustrative components within acommunications device 400 configured to identify abnormal V2Xtransmissions in the face of aforementioned challenges, according to anembodiment of the disclosure. Communications device 400 may beimplemented in a vehicle, as a mobile device carried by a pedestrian, asan infrastructure component, etc. For simplicity of illustration, onlycertain components of the communications device 400 is shown. However,it should be understood that communications device 400 may be part of adevice capable of bi-directional V2X communication. In this example,communications device 400 comprises various components such as a receivewireless transceiver 402, a global navigation satellite system (GNSS)receiver/transceiver 404, additional sensors 406, and processor andmemory 408, among other components. Processor and memory 408 refers toone or more processors and associated memory that are capable ofexecuting programmed instructions to perform various tasks. Theoperation of processor and memory 408 may be organized into blocks,which may include a BSM parser 410, a location estimator 412, and anabnormal transmission detector 414. Once again, a BSM message isdescribed here for illustrative purposes. Other types of messages,including a CAM message or a DENM message, may also be used. While onlyone processor is shown in FIG. 4, the various operations performed byprocessor and memory 408 may be performed by one or more processors indifferent embodiments.

According to an embodiment of the disclosure, communications device 400is capable of receiving a V2X communication and determining whether theV2X communication is an abnormal transmission, utilizing an evaluationagainst an RSSI-to-distance model selected based on prior V2Xcommunications that communications device 400 has received. In variousembodiments, such V2X communications may comprise BSM messages. Forexample, communications device 400 may have recently received a group ofBSM messages, denoted here as BSM_(1A), BSM_(1B), and BSM_(1C), from afirst plurality of transmitting devices (e.g., pedestrian 110, vehicle104, and infrastructure component 112). Wireless transceiver 402receives and demodulates the radio signals containing messages BSM_(1A),BSM_(1B), and BSM_(1C). In doing so, RX radio 40 generates RSSI valuesassociated with the reception of each BSM message—i.e., RSSI_(1A),RSSI_(1B), and RSSI_(1C)—and forwards these RSSI values to the abnormaltransmission detector 414.

Wireless transceiver 402 also generates the demodulated baseband datawhich comprise BSM messages BSM_(1A), BSM_(1B), and BSM_(1C) andforwards the baseband data to the BSM parser 410. BSM parser 410 parsesthe baseband data, taking into account the applicable BSM messagestructure, and extracts useful information from each BSM message. Theextracted information includes location data such as locations LOC_(1A),LOC_(1B), and LOC_(1C), which correspond to the locations of the sendersof BSM messages BSM_(1A), BSM_(1B), and BSM_(1C), respectively. BSMparser 410 forwards locations LOC_(1A), LOC_(1B), and LOC_(1C) to theabnormal transmission detector 414.

GNSS receiver/transceiver/transceiver 404 and additional sensors 406generate data such as satellite positioning fixes, Wifi-based locationfixes, and other sensor-based location data (e.g., camera/image-basedlocation fix) and forward such data to the location estimator 412.Location estimator 412 combines the location data to generate locationestimates for communications device 400 itself. Because communicationsdevice 400 may be a moving entity, such as a vehicle or a pedestrian,location estimates for communications device 400 may vary over time.Location estimator 412 may generate location estimates, denoted asFIX_(1A), FIX_(1B), and FIX_(1C), corresponding to the location ofcommunications device 400 when it received BSM messages BSM_(1A),BSM_(1B), and BSM_(1C), respectively. Location estimator 412 forwardslocation estimates FIX_(1A), FIX_(1B), and FIX_(1C) to the abnormaltransmission detector 414. Using the aforementioned data collected fromvarious components, the abnormal transmission detector 414 may select anRSSI-to-distance relationship model. The process of selecting theRSSI-to-distance relationship model is described in more detail in latersections.

The selected RSSI-to-distance relationship model may then be used todetermine whether a new message, denoted here as BSM₂, from a differenttransmitting device (e.g., pedestrian 108) is a normal or abnormaltransmission. Doing so involves generating and forwarding similar datafor the new message BSM₂. For example, Wireless transceiver 402 mayreceive BSM₂ and generate an RSSI value, RSSI₂, associated withreception of BSM₂. Wireless transceiver 402 may forward RSSI₂ to theabnormal transmission detector 414. Wireless transceiver 402 may alsogenerate the demodulated baseband data comprising message BSM₂ andforward the baseband data to BSM parser 410. BSM parser 410 may extractlocation LOC₂, which corresponds to the location of the sender of themessage BSM₂. BSM parser 410 may forward location LOC₂ to the abnormaltransmission detector 414. GNSS receiver/transceiver/transceiver 404 andadditional sensors 406 may generate various location data to thelocation estimator 412. Location estimator 412 may use the location datato generate a location estimate FIX₂, which corresponds to the locationof communications device 400 when it received message BSM₂. Locationestimator 412 may forward location estimate FIX₂ to the abnormaltransmission detector 414. Having received RSSI₂, LOC₂, and FIX₂, theabnormal transmission detector 414 may use these values to determinewhether BSM₂ is a normal or abnormal transmission, utilizing theselected RSSI-to-distance relationship model.

FIG. 5 is a block diagram illustrating operations of the abnormaltransmission detector 414, according to an embodiment of the disclosure.As shown, the abnormal transmission detector 414 may comprise a distancecompute block 502, a model selector block 504, and a model conformanceevaluator block 506. Distance compute block 502 generally operates byreceiving pairs of locations and computing the distance between the pairof locations. For example, distance compute block 502 may receivelocation LOC_(1A), LOC_(1B), and LOC_(1C), as well as location fixesFIX_(1A), FIX_(1B), and FIX_(1C). Distance compute block 502 maygenerate distances D_(1A), D_(1B), and D_(1C). D_(1A) corresponds to thedistance between LOC_(1A) and FIX_(1A). D_(1B) corresponds to thedistance between LOC_(1B) and FIX_(1B). D_(1C) corresponds to thedistance between LOC_(1C) and FIX_(1C). Thus, distances D_(1A), D_(1B),and D_(1C) represent the distances of transmission associated withBSM_(1A), BSM_(1B), and BSM_(1C), respectively. Model selector 504receives the RSSI values RSSI_(1A), RSSI_(1B), and RSSI_(1C), whichrepresent the received signal strengths associated with the reception ofBSM_(1A), BSM_(1B), and BSM_(1C), respectively. Based on the threeRSSI-to-distance data pairs—i.e., (RSSI_(1A), D_(1A)), (RSSI_(1B),D_(1B)), and (RSSI_(1C), D_(1C)), the model selector 504 selects anRSSI-to-distance relationship model. The model selector 504 may selectthe RSSI-to-distance relationship model from a plurality ofpredetermined RSSI-to-distance relationship models. Here, a“predetermined RSSI-to-distance relationship model” is broadly definedto be any model that characterizes a relationship between RSSI valuesand distance values. Such a model may be “predetermined” in many ways.For example, in one embodiment, the relationship between RSSI values anddistance values may be formed as one or more fixed mathematicalexpressions or constant table look-up values that are determined duringthe manufacturing or factory programming of the equipment in question,such as the abnormal transmission detector 414. In another embodiment,the relationship between RSSI values and distance values may bedetermined by machine learning (ML) models whose structure or parametersmay be determined during the manufacturing or factory programming of theequipment in question. Such an ML model is still considered to be“predetermined,” even if coefficients or other values associated withthe ML model may be updated during use (after manufacturing and factoryprogramming), such as through a process involving unsupervised learning.

FIG. 6 presents a plurality of predetermined RSSI-to-distancerelationship models, according to one embodiment of the disclosure. Eachmodel may correspond to a different signal propagation context. In thisexample, seven predetermined RSSI-to-distance relationship models areshown. These are:

-   -   City Simple Path Loss Model with Path Loss Exponent of 1.5    -   City Simple Path Loss Model with Path Loss Exponent of 2.0    -   City Simple Path Loss Model with Path Loss Exponent of 2.5    -   City Simple Path Loss Model with Nakagami Fading    -   Highway Simple Path Loss Model    -   City Two-ray Model    -   City Breakpoint Model

Here, the various City Simple Path Loss models are based on signalpropagation environments associated with a city, with obstructions suchas buildings, cross traffic, and other entities which can generally addmore obstructions and interference to received signals. The City SimplePath Loss models shown correspond to path loss exponents of 1.5, 2.0,and 2.5, respectively. Each path loss exponent represents differentextent by which signal strength is expected to decrease exponentiallyover distance. The City Simple Path Loss Model with Nakagami Fading is amore nuanced model typically associated with multipath scattering withrelatively large delay-time spreads, with different clusters ofreflected waves, which may be characteristic of interference frommultiple sources in a cellular system. The Highway Simple Path LossModel is based on a signal propagation environment associated withhighways, which may be associated with fewer fixed-location obstructionssuch as buildings. The City Two-ray Model is also based on a signalpropagation environment associated with a city. However, at least twodistinct signal propagation paths are considered. The two paths maycomprise, for example, a direct line-of-sight path and a reflected path,e.g., reflecting off of the road surface. The City Breakpoint Model isalso based on a signal propagation environment associated with a citybut with a breakpoint at a particular distance. For distances up to thebreakpoint, the model may follow one particular fading characteristic,and for distances greater than the breakpoint, the model may follow adifferent fading characteristic. While seven predeterminedRSSI-to-distance relationship models are shown in FIG. 7, a differentnumber of predetermined models may be implemented.

In FIG. 6, each predetermined RSSI-to-distance relationship model isillustrated using a plurality of contour lines. Each RSSI-to-distancerelationship model may be viewed as a distribution of RSSI values ateach distance value. The plurality of contour lines represent theboundaries of bins associated with the RSSI distribution at eachdistance. For example, the City Simple Path Loss Model with Path LossExponent of 1.5 is illustrated using five contour lines. These fivecontour lines represent the boundaries of the six bins used to dividethe distribution of RSSI values at each distance. While six bins areused to characterize each RSSI-to-distance relationship model in theembodiment shown in FIG. 6, a different number of bins may be used.Also, while contour lines are used to illustrate each RSSI-to-distancerelationship model in the embodiment shown, different measures may beused in other embodiments. While FIG. 6 appears to reflect data pointsthat are densely spaced over distance, each RSSI-to-relationship modelcan be represented using data points that are sparsely spaced overdistance. Interpolation and smoothing can be applied to “fill in” valuesbetween sparsely spaced data points. Again, data underlying eachRSSI-to-distance relationship model may be derived from real-worldobservations, simulations, calculations, and/or other sources.

Returning to FIG. 5, model selector block 504 may perform a fit test, inorder to determine which one of the seven predetermined RSSI-to-distancerelationship models best fits a set of RSSI-to-distance observationsassociated with a plurality of first transmitting devices—e.g., datapairs (RSSI_(1A), D_(1A)), (RSSI_(1B), D_(1B)), and (RSSI_(1C), D_(1C)).According to a specific embodiment, a chi-square test (also known as χ²test) is used as the fit test. The chi-square test is a statisticalhypothesis test used to determine whether there is a statisticallysignificant difference between a set of observations and a pluralityhypothesized distributions. Typically, the chi-square test yields a setof P-values. Each P-value represents the degree to which the observationis deemed a good “fit” with a hypothesized distribution. A lower P-valuecorresponds to a better fit, and a higher P-value corresponds to aweaker fit. While a chi-square test is described here for illustrativepurposes, other types of fit tests may be used in accordance withvarious embodiments of the disclosure.

FIG. 7 shows the results of a chi-square test performed to fit a firstset of RSSI-to-distance observations to each of the seven predeterminedRSSI-to-distance relationship models shown in FIG. 6. For example, thefirst set of RSSI-to-distance observations may be made on a morning of aMonday. The RSSI-to-distance observations may comprise a first instanceof the data pairs (RSSI_(1A), D_(1A)), (RSSI_(1B), D_(1B)), and(RSSI_(1C), D_(1C)). The y-axis represents the magnitude of the chistatistic generated, which may comprise the P-values from the chi-squaretest. Here, seven different chi-square test outcomes are plotted, onefor each of the seven predetermined RSSI-to-distance relationshipmodels. As can be seen, six of the models yield P-values that aresignificant in magnitude and grow with distance. By contrast, one model,the Highway Simple Path Loss Model, yields P-values that stay relativelyclose to zero, over a range of distances. The notably low P-valuesindicate a very good fit of the first set of RSSI-to-distanceobservations to the Highway Simple Path Loss Model. Thus, model selector504 may select the Highway Simple Path Loss Model on this Mondaymorning.

FIG. 8 shows the results of a chi-square test performed to fit a secondset of RSSI-to-distance observations to each of the seven predeterminedRSSI-to-distance relationship model shown in FIG. 6. For example, thefirst set of RSSI-to-distance observations may be made on the sameMonday, but in the afternoon. Again, the RSSI-to-distance observationsmay comprise a second instance of the data pairs (RSSI_(1A), D_(1A)),(RSSI_(1B), D_(1B)), and (RSSI_(1C), D_(1C)). The y-axis represents themagnitude of the chi statistic generated, which may comprise theP-values from the chi-square test. Once again, seven differentchi-square test outcomes are plotted, one for each of the sevenpredetermined RSSI-to-distance relationship models. As can be seen, sixof the models yield P-values that are significant in magnitude and growwith distance. By contrast, one model, the City Two-ray Model, yieldsP-values that stay relatively close to zero, over a range of distances.The notably low P-values indicate a very good fit of the first set ofRSSI-to-distance observations to the City Two-ray Model. Thus, modelselector 504 may select the City Two-ray Model on this Monday afternoon.

The Monday morning versus Monday afternoon examples shown in FIGS. 7 and8, respectively, illustrate the adaptive nature of the disclosedtechnique. The selection of the RSSI-to-distance relationship model isbased on recent RSSI-to-distance observations. The signal propagationenvironment may change, as time elapses, as the V2X receiving devicemoves to different locations, and/or as surrounding V2X entities orother entities move or change their behavior. By selecting anappropriate RSSI-to-distance relationship model based on recentobservations, the V2X receiving device may flexibly adapt to the signalpropagation environment as it evolves. The frequency and timing withwhich the model selector 504 selects a new model may depend onimplementation. In some embodiments, model selection may be performed ona fixed, periodic schedule. For example, it may be performed every Mminutes. In other embodiments, model selection may be triggered based onone or more events, such as an indication that the V2X receiving devicehas moved to a different location. In yet other embodiments, modelselection may be performed based on a combination of factors includingtime, location, and/or other considerations.

Returning to FIG. 5, the model conformance evaluator block 506 receivesthe selected RSSI-to-distance relationship model from the modelselection block 504. This may be done in different ways. For example,the model selection block 504 may forward actual data characterizing thedistribution of the selected model, such as the contour linescorresponding to the selected, predetermined RSSI-to-distancerelationship model shown in FIG. 6, to the model conformance evaluatorblock 506. In a different example, each predetermined RSSI-to-distancerelationship model may be associated with an index, for example, and themodel selection block 504 may simply forward the index (e.g., “5”)associated with the selected RSSI-to-distance relationship model (e.g.,Highway Simple Path Loss Model) to the model conformance evaluator block506.

Next, the model conformance evaluator block 506 evaluates anRSSI-to-distance data pair, e.g., (RSSI₂, D₂) shown in FIG. 5,corresponding to a second V2X message (e.g., BSM₂) from a secondtransmitting device against the selected model, to determine whether acriterion for conforming to the selected model is met or has failed. Anexample of such a criterion is whether the RSSI-to-distance data pairfits within a range of acceptable RSSI values established by a maximumand a minimum RSSI curve, such as that shown in FIG. 3. Thus, theconformance evaluator block 506 may determine whether the RSSI₂ valuefalls within a range of acceptable RSSI values between the maximum RSSIvalue and minimum RSSI value defined for distance D₂, according to theselected RSSI-to-distance relationship model (e.g., Highway Simple PathLoss Model). If the criterion is met, then the second V2X message may beidentified as a normal V2X transmission. If on the other hand theRSSI-to-distance data pair fails the criterion, then the second V2Xmessage may be identified as an abnormal V2X transmission.

According to various embodiments, the disclosed techniques exhibit aninherent tamper-resistant characteristic. As discussed, model selectionmay be based on RSSI-to-distance observations made on a plurality offirst V2X messages (e.g., BSM_(1A), BSM_(1B), and BSM_(1C)) from aplurality of first transmitting devices (e.g., pedestrian 110, vehicle104, and infrastructure component 112). Evaluation for abnormal V2Xtransmission, in turn, may be based on an RSSI-to-distance observationmade for a second V2X message (e.g., BSM₂) from a second transmittingdevice (e.g., pedestrian 108) different from the plurality of firsttransmitting devices. Furthermore, the plurality of first transmittingdevices may also represent a diversified group of transmitting devices,further enhancing the tamper-resistant nature of the technique. Thus,the disclosed techniques make it more difficult for the misbehavingtransmitting device to influence the selection of the RSSI-to-distancerelationship model. It is more likely that other V2X transmittingdevices, those that are not misbehaving, would influence the selectionof RSSI-to-distance relationship model—i.e., the “baseline” for what isconsidered to be a normal RSSI-to-distance relationship. Once theRSSI-to-distance relationship model is reliably selected, themisbehaving V2X transmitting device can be identified as abnormal fordeviating from the selected model.

Another benefit of the disclosed techniques is that utilize data thatmay be readily available from an existing communications system. Forexample, RSSI values such as RSSI_(1A), RSSI_(1B), RSSI_(1C), and RSSI₂for BSM messages may already be available from hardware such asblockwireless transceiver 402 shown in FIG. 4. Location data for thetransmitting device typically exist as part of the standard BSM messageformat, so location data such as LOC_(1A), LOC_(1B), LOC_(1C), and LOC₂may already be available and can be parsed from incoming BSM messages.Likewise, the location fix data for the wireless transceiver 402 itself,such as FIX_(1A), FIX_(1B), FIX_(1C), and FIX₂ may already be availablefrom position determining equipment such as GNSSreceiver/transceiver/transceiver 404, additional sensors 406, etc.already present in the communications device 400. Thus, it is likelythat no additional equipment may be needed to implement theaforementioned techniques for identification of abnormal transmissions,according to embodiments of the present disclosure.

FIG. 9A illustrates an unobstructed scenario 900 for V2X signalpropagation between two devices. Here, a vehicle 902 may transmit a V2Xmessage, e.g., a BSM message, to another vehicle 904. There is a direct,unobstructed line-of-sight (LOS) path from the vehicle 902 to thevehicle 904. As such, the V2X message may be sent from the vehicle 902to the vehicle 904 without effects of obstruction. Other environmentalconditions may still impact the propagation of the V2X signal. Forinstance, if the roadway is a city street versus a highway, the signalpropagation channel may differ. If the environment exhibits a two-raypropagation or breakpoint-type propagation behavior, the signalpropagation channel may also differ. Thus, the vehicle 904 may apply thetechniques described herein to select an RSSI-to-distance relationshipmodel from a plurality of predetermined RSSI-to-distance relationshipmodels (e.g., FIGS. 5 and 6), e.g., by performing a “fit” test to findthe most likely model. The vehicle 904 may evaluate the RSSI anddistance values associated with the V2X message received from thevehicle 902 against the selected RSSI-to-distance relationship model todetermine whether the V2X message received from the vehicle 902 is anabnormal transmission. While FIG. 9A illustrates an example of twovehicles as the transmitting device and receiving device, the scenariomay apply to other types of devices such as other vehicles, pedestrianuser equipment, RSUs, other types of device, or any combination thereof.

FIG. 9B illustrates an obstructed scenario 910 for V2X signalpropagation between two devices. Once again, a vehicle 912 may transmita V2X message, e.g., a BSM message, to another vehicle 914. In thiscase, however, a large truck 916 is positioned between the vehicle 912and the vehicle 914. The large truck 916 blocks a direct LOS path 918between the vehicle 912 and the vehicle 914 and complicates the processof finding a “fit” to select an RSSI-to-distance relationship model andusing the selected model to determine whether the V2X message receivedfrom the vehicle 912 is an abnormal transmission.

According to certain embodiments, the vehicle 914 may improve abnormaltransmission detection performance by using one or more sensors torecognize the presence of an intervening obstruction and, in response,modifying the operation to select and/or apply the RSSI-to-distancerelationship model, in order to determine whether the V2X message fromthe vehicle 912 is an abnormal transmission. In such embodiments, afirst step may involve detecting, at the vehicle 914, the presence ofthe large truck 918 between the vehicle 912 and the vehicle 914. Forexample, the vehicle 914 may utilize one or more cameras aboard thevehicle 914 to capture images in the relevant direction. In this case,the vehicle 914 may use one or more forward-facing cameras to captureimages in the direction of the vehicle 912. The vehicle 914 may utilizeobject detection (e.g., machine learning-based detection) to detect thelarge truck 916 within the captured images.

The vehicle 914 may also utilize available information regarding theestimated position(s) of surrounding vehicles, along with sensorreadings, to aid in deciphering the obstructed scenario 910. Forinstance, the vehicle 914 may receive a broadcast BSM message from thevehicle 912 that includes the vehicle type and GPS coordinates of thevehicle 912. Similarly, the vehicle 914 may receive a broadcast BSMmessage from the large truck 916 that includes the vehicle type and GPScoordinates of the large truck 916. Based on such GPS coordinates, thevehicle 914 may be able to link the object, i.e., large truck, detectedin images captured by one or more cameras as the large truck identifiedin the relevant BSM message received. Based on captured images, positiondata such as GPS coordinates, vehicle type information, and/or otherinformation obtained, the vehicle 914 may determine an object in thevicinity of the vehicle 912 and/or the vehicle 914. For example, thevehicle 914 may determine that the object is positioned between thevehicle 912 and the vehicle 914. In a specific example, the vehicle 914may determine whether a detected object is blocking a LOS path betweenthe vehicle 912 and the vehicle 912. Use of additional information suchas BSM messages can thus refine the sensor-based detection of anobstruction such as the vehicle 916.

Additionally or alternatively, the vehicle 914 may utilize other typesof sensors in the detection of an intervening object. For example, thevehicle 914 may utilize readings from sensors such as RADAR, LIDAR,accelerometer, steering wheel angle sensor, etc., to determine theposition and orientation of other vehicles and the vehicle 914 itself.By combining outputs from multiple ones and/or different types ofsensors, the vehicle 914 may improve the detection of any interveningobject(s). Also, the vehicle 914 may utilize additional information suchas steering wheel angle, speed, acceleration, and other data obtainedfrom BSM messages received from surrounding vehicles. The vehicle 914may employ the sensor readings and/or received BSM messages to determinehow many intervening objects there are and types of intervening objectsthat are present.

Having detected an object in the vicinity of the vehicle 912 or thevehicle 914, e.g., a large truck 916 obstructing the LOS between thevehicle 912 and the vehicle 914, the vehicle 914 may take a modifiedapproach for abnormal transmission detection. In one embodiment, thevehicle 914 may modify the criterion for conforming to the selectedRSSI-to-distance model, in determining whether the V2X message is anabnormal transmission. Referring back to FIGS. 4 and 5, the vehicle 914may comprise a model conformance evaluator 506, as part of an abnormaltransmission detector 414. As previously discussed, the modelconformance evaluator 506 may establish a range of RSSI values for anygiven estimated distance between the transmitting device and thereceiving device of the V2X message. The range of RSSI values may bedefined by an upper RSSI value and a lower RSSI value, which may bemodified in response to detecting the intervening object.

For example, in response to detecting the object in the vicinity of thevehicle 912 or the vehicle 914, e.g., the large truck 916 obstructingthe LOS between the vehicle 912 and the vehicle 914, the vehicle 514 mayexpand the range of RSSI values associated with the criterion forconforming to the selected RSSI-to-distance model. The range of RSSIvalues may be expanded by defining (1) a revised upper RSSI value higherthan the previous upper RSSI value and/or (2) a revised lower RSSI valuethat is lower than the previous lower RSSI value. For example, therevised upper RSSI value may be increased by a certain amount, e.g., 2dB to 10 dB, as compared to the previous upper RSSI value. Alternativelyor additionally, the revised lower RSSI value may be decreased by acertain amount, e.g., 2 dB to 10 dB, as compared to the previous lowerRSSI value. Expanding the RSSI range loosens the conformance criterion,which may accommodate an increase in the variance of the received signalstrength that may be associated with the presence of the interveningobject. The degree to which the range of RSSI values is expanded mayalso be modified based on the type and/or number of objects detected.For example, if two large trucks are determined to be obstructing theLOS between the vehicle 912 and the vehicle 914, the revised upper RSSIvalue may be further increased, and the revised lower RSSI value may befurther decreased.

In another example, in response to detecting the object in the vicinityof the vehicle 912 or the vehicle 914, e.g., the large truck 916obstructing the LOS between the vehicle 912 and the vehicle 914, thevehicle 914 may shift the range of RSSI values associated with thecriterion for confirming to the selected RSSI-to-distance model. Here,the vehicle 914 may shift the RSSI range by defining (1) a revised upperRSSI value lower than the previous upper RSSI value and/or (2) a revisedlower RSSI value that is lower than the previous lower RSSI value. Forexample, the revised upper RSSI value may be decreased by a certainamount, e.g., 2 dB to 10 dB, as compared to the previous upper RSSIvalue. Alternatively or additionally, the revised lower RSSI value maybe decreased by a certain amount, e.g., 2 dB to 10 dB, as compared tothe previous lower RSSI value. Shifting the RSSI range downward in thismanner may accommodate a decrease in the overall received signalstrength of the received V2X message, which may be associated withgreater attenuation of the signal in the presence of the interveningobject. The degree to which the range of RSSI values is shifted may alsobe modified based on the type and/or number of objects detected. Forexample, if two large trucks are determined to be obstructing the LOSbetween the vehicle 912 and the vehicle 914, the revised upper RSSIvalue may be further decreased, and the revised lower RSSI value may befurther decreased.

FIG. 10 is an example of a log file 1000 generated by a host vehiclelisting details associated with BSM messages that have been identifiedas abnormal transmissions. Each row of the log file 1000 corresponds toa BSM message that has been received by a host vehicle from a remotevehicle and identified as being an abnormal transmission. The BSMmessages shown may have been transmitted by the same remote vehicle ordifferent remote vehicles. As shown, the log file 1000 includes severalcolumns, including a timestamp column 1002, a host vehicle latitudedegree column 1004, a host vehicle longitude degree column 1006, aremote vehicle latitude column 1008, a remote vehicle longitude column1010, a context column 1012, an received signal strength indicator(RSSI) column 1014, and an RSSI versus distance state column 1016. Thelog file 1000 may be generated, for example, by an on board unit (OBU)of the host vehicle. The log file 1000 is illustrated only as anexample, and other log files generated by a host vehicle or otherreceiving device may vary in the format and types of informationpresented.

The log file 1000 contains sufficient information to reveal the mannerin which each listed BSM message may have been detected as anRSSI-to-distance mismatch and thus identified as an abnormaltransmission. Specifically, an RSSI-to-distance data pair can be derivedfor each BSM message. For each BSM message, the distance between thehost vehicle and the remote vehicle can be computed, based on thelocation of the host vehicle and the location of the remote vehicle. Thehost vehicle latitude degree column 1004 and host vehicle longitudedegree 1006 column may provide the location of the host vehicle, interms of a latitude and a longitude estimate, at the time of thereception of the BSM message by the host vehicle. The remote vehiclelatitude degree column 1008 and the remote vehicle longitude degreecolumn 1010 may provide the location of the remote vehicle, in term of alatitude and a longitude estimate, at the time of the transmission ofthe BSM message by the remote vehicle. As discussed previously, the hostvehicle may extract such remote vehicle latitude and longitudeinformation from the contents of the BSM message. The RSSI column 1014provides the measured RSSI values associated with reception of each BSMmessage. Thus, information provided in the log file 1000 can be used toderive an RSSI-to-distance data pair for each BSM message listed.

The RSSI-to-distance data pair associated with each BSM message may beevaluated to determine whether it meets or fails a criterion forconforming to a known RSSI-to-distance relationship model. For example,referring back to FIG. 3, the RSSI-to-distance relationship model mayindicate a conformance range defined by a “maximum” and a “minimum” RSSIvalue for any given distance. If the RSSI value indicated by the RSSIcolumn 1014 falls outside the conformance range (i.e., greater than themaximum or less than the minimum RSSI value) established for computeddistance between the host vehicle and the remote vehicle, it can beconcluded that such failure to meet the conformance criterion likely ledto the indication an “RSSI_VS DISTANCE_MISMATCH” value in the RSSIversus Distance State column 1016.

Using a log file such as the log file 1000, a receiving device (e.g., ahost vehicle) may be tested to determine whether it implements aspectsof the present disclosure. For example, the testing scenario may involvetransmitting a plurality of V2X messages (e.g., BSM messages) from aplurality of transmitting devices (e.g., one or more remote vehicles) tothe receiving device. The receiving device would receive the pluralityof V2X messages. The plurality of V2X messages may correspond to aplurality of RSSI-to-distance data pairs, including an RSSI-to-distancedata pair for each of the plurality of V2X messages. EachRSSI-to-distance data pair can be carefully controlled. For example,each V2X message may be sent to the receiving device at a preciselyadjusted received signal strength. Also, each V2X message (e.g., BSMmessage) may be formulated to contain a sender location (e.g., latitudeand longitude) of the transmitting device that corresponds to a knowndistance between the receiving device and the transmitting device. Theplurality of RSSI-to-distance data pairs may thus be designed tocorrespond to a known RSSI-to-distance relationship model. Havingreceived such a plurality of V2X messages, a receiving device thatimplements aspects of the present disclosure would likely select theknown RSSI-to-distance relationship model.

Next, the testing scenario may involve transmitting an additional V2Xmessage, from a transmitting device different from the plurality oftransmitting devices, to the receiving device. The receiving devicewould receive the additional V2X mesge. The additional V2X message maycorresponds to an additional RSSI-to-distance data pair. Here, theadditional RSSI-to-distance data pair may be purposely designed to failthe criterion for conforming to the known RSSI-to-distance relationshipmodel. In response to receiving such an additional V2X message, areceiving device that implements aspects of the present disclosure maybe likely to identify the additional V2X message as an abnormaltransmission. For example, the receiving device may generate an entry ina log file indicating that an RSSI-versus-distance mismatch has beendetected—e.g., an “RSSI_VS DISTANCE_MISMATCH” value in the RSSI versusDistance State column 1016.

Furthermore, context information associated with each BSM message, asreported by the host vehicle, may further confirm the RSSI-to-distancerelation model that has been selected by a receiving device such as thehost vehicle. As shown, the context column 1012 provides the context,e.g., driving environment, reported by the remote vehicle at the time ofthe reception of the BSM message. For example, the context column mayshow a values such as “Highway,” “Urban,” or other values. The hostvehicle may be able obtain its context information from a contextdetection module, which may use various forms of input to detect thecontext of the host vehicle. Just as an example, the context detectionmodule may utilize the location of host vehicle and available mapinformation to determine whether the host vehicle is traveling on ahighway or in an urban setting. The contexts detection module may alsoutilize inputs such as available weather information and rain sensormeasurements to determine the environmental and road conditions, whichcan also be taken into account in determining the context. Additionalsensor and/or other inputs may further refine the context determination.

FIG. 11 is a flowchart illustrating a process 1100 for abnormaltransmission identification, according to an embodiment of thedisclosure. In a step 1102, at a receiving device, a plurality of V2Xmessages are received from a plurality of transmitting devices. Forexample, BSM messages BSM_(1A), BSM_(1B), and BSM_(1C) may be receivedfrom pedestrian 110, vehicle 104, and infrastructure component 112 shownin FIG. 1. In a step 1104, a plurality of RSSI-to-distance data pairsare obtained, including an RSSI-to-distance data pair for each of theplurality of V2X messages. For example, three such RSSI-to distance datapairs may be (RSSI_(1A), D_(1A)), (RSSI_(1B), D_(1B)), and (RSSI_(1C),D_(1C)), shown in FIG. 5. In a step 1106, an RSSI-to-distancerelationship model is established, based, at least in part, on theplurality of RSSI-to-distance data pairs. For example, model selectorblock 504 shown in FIG. 5 may select the predetermined RSSI-to-distancerelationship model, based on RSSI values RSSI_(1A), RSSI_(1B), andRSSI_(1C), etc. and distance values D_(1A), D_(1B), and D_(1C), etc. Ina step 1108, at the receiving device, an additional V2X message may bereceived from a transmitting device different from the plurality oftransmitting devices. For example, BSM message BSM₂ may be received frompedestrian 108 shown in FIG. 1. In a step 1110, an additionalRSSI-to-distance data pair may be obtained for the additional V2Xmessage. For example, such an RSSI-to-distance data pair may be (RSSI₂,D₂), shown in FIG. 5. In a step 1112, the additional RSSI-to-distancedata pair may be compared to the RSSI-to-distance relationship model.Such a comparison may be made by the model conformance evaluator block506 in FIG. 5, for example. Finally, in a step 1114, in response todetermining that the additional RSSI-to-distance data pair fails acriterion for conforming to the RSSI-to-distance relationship model, theadditional V2X message may be identified as an abnormal transmission.Such an identification of the abnormal transmission may be made by themodel conformance evaluator block 506 in FIG. 5.

FIG. 12 is a block diagram of various hardware and software componentsof a vehicle 1200, according to an embodiment. An example of vehicle1200 may be vehicle 102 shown in FIG. 1. Vehicle 1200 may comprisecommunication device such as communications device 400 shown in FIG. 4.While a vehicle is described here for illustrative purposes, othertransceiver receiving V2X communications, such as a device carried by apedestrian or an infrastructure component, may implement the disclosedtechniques for identifying abnormal transmissions. Returning to FIG. 12,vehicle 1200 may comprise for example, a car, truck, motorcycle and/orother motorized vehicle, may transmit radio signals to, and receiveradio signals from, other vehicles, for example, via V2X car to carcommunication, and/or from a wireless communication network, basestation, and/or wireless access point, etc. In one example, vehicle 1200may communicate, via wireless transceiver(s) 1230 and wirelessantenna(s) 1232 with other vehicles and/or wireless communicationnetworks by transmitting wireless signals to, or receiving wirelesssignals from a remote wireless transceiver which may comprise anothervehicle, a base station (e.g., a NodeB, eNodeB, or gNodeB) or wirelessaccess point, over a wireless communication link.

Similarly, vehicle 1200 may transmit wireless signals to, or receivewireless signals from a local transceiver over a wireless communicationlink, for example, by using a WLAN and/or a PAN wireless transceiver,here represented by one of wireless transceiver(s) 1230 and wirelessantenna(s) 1232. In an embodiment, wireless transceiver(s) 1230 maycomprise various combinations of WAN, WLAN, and/or PAN transceivers. Inan embodiment, wireless transceiver(s) 1230 may also comprise aBluetooth transceiver, a ZigBee transceiver, or other PAN transceiver.In an embodiment, vehicle 1200 may transmit wireless signals to, orreceive wireless signals from a wireless transceiver 1230 on a vehicle1200 over wireless communication link 1234. A local transceiver, a WANwireless transceiver and/or a mobile wireless transceiver may comprise aWAN transceiver, an access point (AP), femtocell, Home Base Station,small cell base station, HNB, HeNB, or gNodeB and may provide access toa wireless local area network (WLAN, e.g., IEEE 802.11 network), awireless personal area network (PAN, e.g., Bluetooth network) or acellular network (e.g., an LTE network or other wireless wide areanetwork such as those discussed in the next paragraph). Of course, itshould be understood that these are merely examples of networks that maycommunicate with a vehicle over a wireless link, and claimed subjectmatter is not limited in this respect. It is also understood thatwireless transceiver(s) 1230 may be located on various types of vehicles1200, such as boats, ferries, cars, buses, drones, and various transportvehicles. In an embodiment, the vehicle 1200 may be utilized forpassenger transport, package transport or other purposes. In anembodiment, GNSS signals 1274 from GNSS Satellites are utilized byvehicle 1200 for location determination and/or for the determination ofGNSS signal parameters and demodulated data. In an embodiment, signals1234 from WAN transceiver(s), WLAN and/or PAN local transceivers areused for location determination, alone or in combination with GNSSsignals 1274.

Examples of network technologies that may support wireless transceivers1230 are GSM, CDMA, WCDMA, LTE, 5G or New Radio Access Technology (NR),HRPD, and V2X car-to-car communication. As noted, V2X communicationprotocols may be defined in various standards such as SAE and ETS-ITSstandards. GSM, WCDMA and LTE are technologies defined by 3GPP. CDMA andHRPD are technologies defined by the 3rd Generation Partnership ProjectII (3GPP2). WCDMA is also part of the Universal MobileTelecommunications System (UMTS) and may be supported by an HNB.

Wireless transceivers 1230 may communicate with communications networksvia WAN wireless base stations which may comprise deployments ofequipment providing subscriber access to a wireless telecommunicationnetwork for a service (e.g., under a service contract). Here, a WANwireless base station may perform functions of a WAN or cell basestation in servicing subscriber devices within a cell determined based,at least in part, on a range at which the WAN wireless base station iscapable of providing access service. Examples of WAN base stationsinclude GSM, WCDMA, LTE, CDMA, HRPD, Wi-Fi, Bluetooth, WiMAX, 5G NR basestations. In an embodiment, further wireless base stations may comprisea WLAN and/or PAN transceiver.

In an embodiment, vehicle 1200 may contain one or more cameras 1235. Inan embodiment, the camera may comprise a camera sensor and mountingassembly. Different mounting assemblies may be used for differentcameras on vehicle 1200. For example, front facing cameras may bemounted in the front bumper, in the stem of the rear-view mirrorassembly or in other front facing areas of the vehicle 1200. Rear facingcameras may be mounted in the rear bumper/fender, on the rearwindshield, on the trunk or other rear facing areas of the vehicle. Theside facing mirrors may be mounted on the side of the vehicle such asbeing integrated into the mirror assembly or door assemblies. Thecameras may provide object detection and distance estimation,particularly for objects of known size and/or shape (e.g., a stop signand a license plate both have standardized size and shape) and may alsoprovide information regarding rotational motion relative to the axis ofthe vehicle such as during a turn. When used in concert with the othersensors, the cameras may both be calibrated through the use of othersystems such as through the use of LIDAR, wheel tick/distance sensors,and/or GNSS to verify distance traveled and angular orientation. Thecameras may similarly be used to verify and calibrate the other systemsto verify that distance measurements are correct, for example bycalibrating against known distances between known objects (landmarks,roadside markers, road mile markers, etc.) and also to verify thatobject detection is performed accurately such that objects areaccordingly mapped to the correct locations relative to the car by LIDARand other system. Similarly, when combined with, for example,accelerometers, impact time with road hazards, may be estimated (elapsedtime before hitting a pot hole for example) which may be verifiedagainst actual time of impact and/or verified against stopping models(for example, compared against the estimated stopping distance ifattempting to stop before hitting an object) and/or maneuvering models(verifying whether current estimates for turning radius at current speedand/or a measure of maneuverability at current speed are accurate in thecurrent conditions and modified accordingly to update estimatedparameters based on camera and other sensor measurements).

Accelerometers, gyros and magnetometers 1240, in an embodiment, may beutilized to provide and/or verify motion and directional information.Accelerometers and gyros may be utilized to monitor wheel and drivetrain performance. Accelerometers, in an embodiment, may also beutilized to verify actual time of impact with road hazards such as potholes relative to predicted times based on existing stopping andacceleration models as well as steering models. Gyros and magnetometersmay, in an embodiment, be utilized to measure rotational status of thevehicle as well as orientation relative to magnetic north, respectively,and to measure and calibrate estimates and/or models for turning radiusat current speed and/or a measure of maneuverability at current speed,particularly when used in concert with measurements from other externaland internal sensors such as other sensors 1245 such as speed sensors,wheel tick sensors, and/or odometer measurements.

LIDAR 1250 uses pulsed laser light to measure ranges to objects. Whilecameras may be used for object detection, LIDAR 1250 provides a means,to detect the distances (and orientations) of the objects with morecertainty, especially in regard to objects of unknown size and shape.LIDAR 1250 measurements may also be used to estimate rate of travel,vector directions, relative position and stopping distance by providingaccurate distance measurements and delta distance measurements.

Memory 1260 may be utilized with processor 1210 and/or DSP 1220, whichmay comprise Random Access Memory (RAM), Read-Only Memory (ROM), discdrive, FLASH, or other memory devices or various combinations thereof.In an embodiment, memory 1260 may contain instructions to implementvarious methods described throughout this description including, forexample, processes to implement the use of relative positioning betweenvehicles and between vehicles and external reference objects such asroadside units. In an embodiment, memory may contain instructions foroperating and calibrating sensors, and for receiving map, weather,vehicular (both vehicle 1200 and surrounding vehicles, e.g., HV 110 andRVs 130) and other data, and utilizing various internal and externalsensor measurements and received data and measurements to determinedriving parameters such as relative position, absolute position,stopping distance, acceleration and turning radius at current speedand/or maneuverability at current speed, inter-car distance, turninitiation/timing and performance, and initiation/timing of drivingoperations.

In an embodiment, power and drive systems (generator, battery,transmission, engine) and related systems 1275 and systems (brake,actuator, throttle control, steering, and electrical) 1255 may becontrolled by the processor(s) and/or hardware or software or by anoperator of the vehicle or by some combination thereof. The systems(brake, actuator, throttle control, steering, electrical, etc.) 1255 andpower and drive or other systems 1275 may be utilized in conjunctionwith performance parameters and operational parameters, to enableautonomously (and manually, relative to alerts and emergencyoverrides/braking/stopping) driving and operating a vehicle 1200 safelyand accurately, such as to safely, effectively and efficiently mergeinto traffic, stop, accelerate and otherwise operate the vehicle 1200.In an embodiment, input from the various sensor systems such as camera1235, accelerometers, gyros and magnetometers 1240, LIDAR 1250, GNSSreceiver/transceiver/transceiver 1270, RADAR 1253, input, messagingand/or measurements from wireless transceiver(s) 1230 and/or othersensors 1245 or various combinations thereof, may be utilized byprocessor 1210 and/or DSP 1220 or other processing systems to controlpower and drive systems 1275 and systems (brake actuator, throttlecontrol, steering, electrical, etc.) 1255.

A global navigation satellite system (GNSS) receiver 1270 may beutilized to determine position relative to the earth (absolute position)and, when used with other information such as measurements from otherobjects and/or mapping data, to determine position relative to otherobjects such as relative to other vehicles and/or relative to the roadsurface. To determine position, the GNSSreceiver/transceiver/transceiver 1270, may receive RF signals 1274 fromGNSS satellites (e.g., RF signals 812 from GNSS satellites 810) usingone or more antennas 1272 (which, depending on functional requirements,may be the same as antennas 1232). The GNSSreceiver/transceiver/transceiver 1270 may support one or more GNSSconstellations as well as other satellite-based navigation systems. Forexample, in an embodiment, GNSS receiver/transceiver/transceiver 1270may support global navigation satellite systems such as GPS, theGLONASS, Galileo, and/or BeiDou, or any combination thereof. In anembodiment, GNSS receiver/transceiver 1270 may support regionalnavigation satellite systems such as NavIC or QZSS or a combinationthereof as well as various augmentation systems (e.g., Satellite BasedAugmentation Systems (SBAS) or ground based augmentation systems (GBAS))such as Doppler Orbitography and Radio-positioning Integrated bySatellite (DORIS) or wide area augmentation system (WAAS) or theEuropean geostationary navigation overlay service (EGNOS) or themulti-functional satellite augmentation system (MSAS) or the local areaaugmentation system (LAAS). In an embodiment, GNSSreceiver/transceiver(s) 1230 and antenna(s) 1232 may support multiplebands and sub-bands such as GPS L1, L2 and L5 bands, Galileo E1, E5, andE6 bands, Compass (BeiDou) B1, B3 and B2 bands, GLONASS G1, G2 and G3bands, and QZSS L1C, L2C and L5-Q bands.

The GNSS receiver/transceiver 1270 may be used to determine location andrelative location which may be utilized for location, navigation, and tocalibrate other sensors, when appropriate, such as for determiningdistance between two time points in clear sky conditions and using thedistance data to calibrate other sensors such as the odometer and/orLIDAR. In an embodiment, GNSS-based relative locations, based on, forexample shared Doppler and/or pseudorange measurements between vehicles,may be used to determine highly accurate distances between two vehicles,and when combined with vehicle information such as shape and modelinformation and GNSS antenna location, may be used to calibrate,validate and/or affect the confidence level associated with informationfrom LIDAR, camera, RADAR, SONAR and other distance estimationtechniques. GNSS Doppler measurements may also be utilized to determinelinear motion and rotational motion of the vehicle or of the vehiclerelative to another vehicle, which may be utilized in conjunction withgyro and/or magnetometer and other sensor systems to maintaincalibration of those systems based upon measured location data. RelativeGNSS positional data may also be combined with high confidence absolutelocations from RSUs, to determine high confidence absolute locations ofthe vehicle. Furthermore, relative GNSS positional data may be usedduring inclement weather that may obscure LIDAR and/or camera-based datasources to avoid other vehicles and to stay in the lane or otherallocated road area. For example, using an RSU equipped with GNSSreceiver/transceiver and V2X capability, GNSS measurement data may beprovided to the vehicle, which, if provided with an absolute location ofthe RSU, may be used to navigate the vehicle relative to a map, keepingthe vehicle in lane and/or on the road, in spite of lack of visibility.

RADAR 1253, uses transmitted radio waves that are reflected off ofobjects. The reflected radio waves are analyzed, based on the time takenfor reflections to arrive and other signal characteristics of thereflected waves to determine the location of nearby objects. RADAR 1253may be utilized to detect the location of nearby cars, roadside objects(signs, other vehicles, pedestrians, etc.) and will generally enabledetection of objects even if there is obscuring weather such as snow,rail or hail. Thus, RADAR 1253 may be used to complement LIDAR 1250systems and camera 1235 systems in providing ranging information toother objects by providing ranging and distance measurements andinformation when visual-based systems typically fail. Furthermore, RADAR1253 may be utilized to calibrate and/or sanity check other systems suchas LIDAR 1250 and camera 1235. Ranging measurements from RADAR 1253 maybe utilized to determine/measure stopping distance at current speed,acceleration, maneuverability at current speed and/or turning radius atcurrent speed and/or a measure of maneuverability at current speed. Insome systems, ground penetrating RADAR may also be used to track roadsurfaces via, for example, RADAR-reflective markers on the road surfaceor terrain features such as ditches.

It will be apparent to those skilled in the art that substantialvariations may be made in accordance with specific requirements. Forexample, customized hardware might also be used, and/or particularelements might be implemented in hardware, software (including portablesoftware, such as applets, etc.), or both. Further, connection to othercomputing devices such as network input/output devices may be employed.

With reference to the appended figures, components that can includememory (e.g., memory 1260 of FIG. 12) can include non-transitorymachine-readable media. The term “machine-readable medium” and“computer-readable medium” as used herein, refer to any storage mediumthat participates in providing data that causes a machine to operate ina specific fashion. In embodiments provided hereinabove, variousmachine-readable media might be involved in providing instructions/codeto processing units and/or other device(s) for execution. Additionallyor alternatively, the machine-readable media might be used to storeand/or carry such instructions/code. In many implementations, acomputer-readable medium is a physical and/or tangible storage medium.Such a medium may take many forms, including, but not limited to,non-volatile media, volatile media, and transmission media. Common formsof computer-readable media include, for example, magnetic and/or opticalmedia, any other physical medium with patterns of holes, a RAM, a PROM,EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier waveas described hereinafter, or any other medium from which a computer canread instructions and/or code.

The methods, systems, and devices discussed herein are examples. Variousembodiments may omit, substitute, or add various procedures orcomponents as appropriate. For instance, features described with respectto certain embodiments may be combined in various other embodiments.Different aspects and elements of the embodiments may be combined in asimilar manner. The various components of the figures provided hereincan be embodied in hardware and/or software. Also, technology evolvesand, thus, many of the elements are examples that do not limit the scopeof the disclosure to those specific examples.

It has proven convenient at times, principally for reasons of commonusage, to refer to such signals as bits, information, values, elements,symbols, characters, variables, terms, numbers, numerals, or the like.It should be understood, however, that all of these or similar terms areto be associated with appropriate physical quantities and are merelyconvenient labels. Unless specifically stated otherwise, as is apparentfrom the discussion above, it is appreciated that throughout thisSpecification discussions utilizing terms such as “processing,”“computing,” “calculating,” “determining,” “ascertaining,”“identifying,” “associating,” “measuring,” “performing,” or the likerefer to actions or processes of a specific apparatus, such as a specialpurpose computer or a similar special purpose electronic computingdevice. In the context of this Specification, therefore, a specialpurpose computer or a similar special purpose electronic computingdevice is capable of manipulating or transforming signals, typicallyrepresented as physical electronic, electrical, or magnetic quantitieswithin memories, registers, or other information storage devices,transmission devices, or display devices of the special purpose computeror similar special purpose electronic computing device.

Terms, “and” and “or” as used herein, may include a variety of meaningsthat also is expected to depend at least in part upon the context inwhich such terms are used. The term “one or more” as used herein may beused to describe any feature, structure, or characteristic in thesingular or may be used to describe some combination of features,structures, or characteristics. However, it should be noted that this ismerely an illustrative example and claimed subject matter is not limitedto this example. Furthermore, the term “at least one of” if used toassociate a list, such as A, B, or C, can be interpreted to mean anycombination of A, B, and/or C, such as A, AB, AA, AAB, AABBCCC, etc.

Having described several embodiments, various modifications, alternativeconstructions, and equivalents may be used without departing from thespirit of the disclosure. For example, the above elements may merely bea component of a larger system, wherein other rules may take precedenceover or otherwise modify the application of the various embodiments.Also, a number of steps may be undertaken before, during, or after theabove elements are considered. Accordingly, the above description doesnot limit the scope of the disclosure.

Implementation examples are described in the following numbered clauses:

Clause 1. A method for abnormal transmission identification, comprising:

at a receiving device, receiving a plurality of V2X messages from aplurality of transmitting devices;

obtaining a plurality of RSSI-to-distance data pairs, including anRSSI-to-distance data pair for each of the plurality of V2X messages;

establishing an RSSI-to-distance relationship model based, at least inpart, on the plurality of RSSI-to-distance data pairs;

at the receiving device, receiving an additional V2X message from atransmitting device different from the plurality of transmittingdevices;

obtaining an additional RSSI-to-distance data pair for the additionalV2X message;

comparing the additional RSSI-to-distance data pair to theRSSI-to-distance relationship model; and

in response to determining that the additional RSSI-to-distance datapair fails a criterion for conforming to the RSSI-to-distancerelationship model, identifying the additional V2X message as anabnormal transmission.

Clause 2. The method of clause 1, wherein establishing theRSSI-to-distance relationship model comprises selecting a predeterminedRSSI-to-distance relationship model from a plurality of predeterminedRSSI-to-distance relationship models.

Clause 3. The method of clause 2, wherein the selecting thepredetermined RSSI-to-distance relationship model from the plurality ofpredetermined RSSI-to-distance relationship models comprises:

for each predetermined RSSI-to-distance relationship model from theplurality of predetermined RSSI-to-distance relationship models,applying a chi-square test to evaluate a fit between (1) the pluralityof RSSI-to-distance data pairs for the plurality of V2X messages and (2)the predetermined RSSI-to-distance relationship model, to generate a fitscore; and

based on the fit score for each predetermined RSSI-to-distancerelationship model from the plurality of predetermined RSSI-to-distancerelationship models, choosing the selected, predeterminedRSSI-to-distance relationship model as a best-fitting model.

Clause 4. The method of clause 2 or 3, wherein the plurality ofpredetermined RSSI-to-distance relationship models correspond to aplurality of signal propagation contexts.

Clause 5. The method of clause 4, wherein the plurality of predeterminedRSSI-to-distance relationship models include at least one of: a citysimple path loss model, a highway simple path loss model, a city two-raymodel, or a city breakpoint model.

Clause 6. The method of any of clauses 2-5, wherein selection of thepredetermined RSSI-to-distance relationship model is repeatedlyperformed based on a time schedule.

Clause 7. The method of any of clauses 2-5, wherein selection of thepredetermined RSSI-to-distance relationship model is repeatedlyperformed based on an event trigger.

Clause 8. The method of any of clauses 1-7, wherein:

each of the plurality of RSSI-to-distance data pairs comprises (1) areceived signal strength indicator (RSSI) value and (2) an estimateddistance between the receiving device and a transmitting device of theV2X message,

the additional RSSI-to-distance data pair comprises (1) an RSSI valueand (2) an estimated distance between the receiving device and thetransmitting device of the additional V2X message.

Clause 9. The method of clause 8 wherein, for each V2X message of theplurality of V2X messages and the additional V2X message:

the V2X message comprises a Basic Safety Message (BSM), CooperativeAwareness Message (CAM), and/or Decentralized Environmental NotificationMessage (DENM),

the RSSI value is associated with reception of the BSM/CAM/DENM message,and

the estimated distance is based in part on location data contained inthe BSM/CAM/DENM message.

Clause 10. The method of clause 9, wherein the estimated distance iscomputed from (1) the location data contained in the BSM/CAM/DENMmessage and (2) a location fix for the receiving device.

Clause 11. The method of any of clauses 1-10, wherein the determiningthat the additional RSSI-to-distance data pair fails the criterion forconforming to the RSSI-to-distance relationship model comprises:

establishing, for an estimated distance for the additional V2X message,an upper RSSI value and a lower RSSI value corresponding to theRSSI-to-distance relationship model; and

determining that an RSSI value for the additional V2X message fallsoutside an RSSI range established by the upper RSSI value and the lowerRSSI value.

Clause 12. The method of any of clauses 1-11, wherein the receivingdevice is part of a vehicular system.

Clause 13. The method of any of clauses 1-11, wherein the receivingdevice is part of an infrastructure system.

Clause 14. The method of any of clauses 1-11, wherein the receivingdevice is part of a mobile device carried by a pedestrian.

Clause 15. The method of any of clauses 1-14, further comprising:

detecting an object in a vicinity of the transmitting device or thereceiving device based on readings obtained from one or more sensors,

wherein the criterion for conforming to the RSSI-to-distancerelationship model is based, at least in part, on detecting the objectin the vicinity of the transmitting device or the receiving device.

Clause 16. The method of clause 15, wherein detecting the object in thevicinity of the transmitting device or the receiving device comprisesdetermining that the object obstructs a line of sight (LOS) between thetransmitting device and the receiving device.

Clause 17. The method of clause 15, wherein the criterion for conformingto the RSSI-to-distance relationship model is associated with an RSSIrange, and

the RSSI range is expanded or shifted in response to detecting theobject in the vicinity of the transmitting device or the receivingdevice.

Clause 18. A method for abnormal transmission identification,comprising:

at a receiving device, receiving a plurality of V2X messages from aplurality of transmitting devices,

wherein the plurality of V2X messages correspond to a plurality ofRSSI-to-distance data pairs, including an RSSI-to-distance data pair foreach of the plurality of V2X messages,

wherein the plurality of RSSI-to-distance data pairs correspond to anRSSI-to-distance relationship model;

at the receiving device, receiving an additional V2X message from atransmitting device different from the plurality of transmittingdevices,

wherein the additional V2X message corresponds to an additionalRSSI-to-distance data pair, and the additional RSSI-to-distance datapair fails a criterion for conforming to the RSSI-to-distancerelationship model; and

at the receiving device, identifying the additional V2X message as anabnormal transmission.

Clause 19. An apparatus for abnormal transmission identification,comprising:

a receive radio unit configured to receive a plurality of V2X messagesfrom a plurality of transmitting devices and receive an additional V2Xmessage from a transmitting device different from the plurality oftransmitting devices; and

one or more processors configured to:

obtain a plurality of RSSI-to-distance data pairs, including anRSSI-to-distance data pair for each of the plurality of V2X messages;

establish an RSSI-to-distance relationship model based, at least inpart, on the plurality of RSSI-to-distance data pairs;

obtain an additional RSSI-to-distance data pair for the additional V2Xmessage;

compare the additional RSSI-to-distance data pair to theRSSI-to-distance relationship model; and

in response to determining that the additional RSSI-to-distance datapair fails a criterion for conforming to the RSSI-to-distancerelationship model, identify the additional V2X message as an abnormaltransmission.

Clause 20. The apparatus of clause 19, wherein the one or moreprocessors are configured to establish the RSSI-to-distance relationshipmodel by selecting a predetermined RSSI-to-distance relationship modelfrom a plurality of predetermined RSSI-to-distance relationship models.

Clause 21. The apparatus of clause 20, wherein the one or moreprocessors are configured to select the predetermined RSSI-to-distancerelationship model from the plurality of predetermined RSSI-to-distancerelationship models by:

for each predetermined RSSI-to-distance relationship model from theplurality of predetermined RSSI-to-distance relationship models,applying a chi-square test to evaluate a fit between (1) the pluralityof RSSI-to-distance data pairs for the plurality of V2X messages and (2)the predetermined RSSI-to-distance relationship model, to generate a fitscore; and

based on the fit score for each predetermined RSSI-to-distancerelationship model from the plurality of predetermined RSSI-to-distancerelationship models, choosing the selected, predeterminedRSSI-to-distance relationship model as a best-fitting model.

Clause 22. The apparatus of clause 20 or 21, wherein the plurality ofpredetermined RSSI-to-distance relationship models correspond to aplurality of signal propagation contexts.

Clause 23. The apparatus of clause 22, wherein the plurality ofpredetermined RSSI-to-distance relationship models include at least oneof: a city simple path loss model, a highway simple path loss model, acity two-ray model, or a city breakpoint model.

Clause 24. The apparatus of any of clauses 20-23, wherein the one ormore processors are configured to repeated select the predeterminedRSSI-to-distance relationship model, based on a time schedule.

Clause 25. The apparatus of any of clauses 20-23, wherein the one ormore processors are configured to repeated select the predeterminedRSSI-to-distance relationship model, based on an event trigger.

Clause 26. The apparatus of any of clauses 19-25, wherein:

each of the plurality of RSSI-to-distance data pairs comprises (1) areceived signal strength indicator (RSSI) value and (2) an estimateddistance between the apparatus and a transmitting device of the V2Xmessage,

the additional RSSI-to-distance data pair comprises (1) an RSSI valueand (2) an estimated distance between the apparatus and the transmittingdevice of the additional V2X message.

Clause 27. The apparatus of clause 26 wherein, for each V2X message ofthe plurality of V2X messages and the additional V2X message:

the V2X message comprises a Basic Safety Message (BSM), CooperativeAwareness Message (CAM), and/or Decentralized Environmental NotificationMessage (DENM),

the RSSI value is associated with reception of the BSM/CAM/DENM message,and

the estimated distance is based in part on location data contained inthe BSM/CAM/DENM message.

Clause 28. The apparatus of clause 27, wherein the one or moreprocessors are configured to compute the estimated distance from (1) thelocation data contained in the BSM/CAM/DENM message and (2) a locationfix for the apparatus.

Clause 29. The apparatus of any of clauses 19-28, wherein the one ormore processors are configured to determine that the additionalRSSI-to-distance data pair fails the criterion for conforming to theRSSI-to-distance relationship, by:

establishing, for an estimated distance for the additional V2X message,an upper RSSI value and a lower RSSI value corresponding to theRSSI-to-distance relationship model; and

determining that an RSSI value for the additional V2X message fallsoutside an RSSI range established by the upper RSSI value and the lowerRSSI value.

Clause 30. The apparatus of any of clauses 19-29, wherein the apparatusis part of a vehicular system.

Clause 31. The apparatus of any of clauses 19-29, wherein the apparatusis part of an infrastructure system.

Clause 32. The apparatus of any of clauses 19-29, wherein the apparatusis part of a mobile device carried by a pedestrian.

Clause 33. The apparatus of any of clauses 19-32, further comprising:

detecting an object in a vicinity of the transmitting device or theapparatus based on readings obtained from one or more sensors,

wherein the criterion for conforming to the RSSI-to-distancerelationship model is based, at least in part, on detecting the objectin the vicinity of the transmitting device or the apparatus.

Clause 34. The apparatus of clause 33, wherein detecting the object inthe vicinity of the transmitting device or the apparatus comprisesdetermining that the object obstructs a line of sight (LOS) between thetransmitting device and the apparatus.

Clause 35. The apparatus of clause 33, wherein the criterion forconforming to the RSSI-to-distance relationship model is associated withan RSSI range, and

the RSSI range is expanded or shifted in response to detecting theobject in the vicinity of the transmitting device or the apparatus.

Clause 36. An apparatus for abnormal transmission identification,comprising:

a receive radio unit configured to receive a plurality of V2X messagesfrom a plurality of transmitting devices and receive an additional V2Xmessage from a transmitting device different from the plurality oftransmitting devices, wherein:

the plurality of V2X messages correspond to a plurality ofRSSI-to-distance data pairs, including an RSSI-to-distance data pair foreach of the plurality of V2X messages,

the plurality of RSSI-to-distance data pairs correspond to anRSSI-to-distance relationship model, and

the additional V2X message corresponds to an additional RSSI-to-distancedata pair, and the additional RSSI-to-distance data pair fails acriterion for conforming to the RSSI-to-distance relationship model; and

one or more processors configured to identify the additional V2X messageas an abnormal transmission.

Clause 37. A system for abnormal transmission identification,comprising:

means for, at a receiving device, receiving a plurality of V2X messagesfrom a plurality of transmitting devices;

means for obtaining a plurality of RSSI-to-distance data pairs,including an RSSI-to-distance data pair for each of the plurality of V2Xmessages;

means for establishing an RSSI-to-distance relationship model based, atleast in part, on the plurality of RSSI-to-distance data pairs;

means for, at the receiving device, receiving an additional V2X messagefrom a transmitting device different from the plurality of transmittingdevices;

means for obtaining an additional RSSI-to-distance data pair for theadditional V2X message;

means for comparing the additional RSSI-to-distance data pair to theRSSI-to-distance relationship model; and

means for, in response to determining that the additionalRSSI-to-distance data pair fails a criterion for conforming to theRSSI-to-distance relationship model, identifying the additional V2Xmessage as an abnormal transmission

Clause 38. The system of clause 37, wherein the means for establishingthe RSSI-to-distance relationship model comprises means for selecting apredetermined RSSI-to-distance relationship model from a plurality ofpredetermined RSSI-to-distance relationship models.

Clause 39. The system of clause 38, wherein the means for selecting thepredetermined RSSI-to-distance relationship model from the plurality ofpredetermined RSSI-to-distance relationship models comprises:

means for, for each predetermined RSSI-to-distance relationship modelfrom the plurality of predetermined RSSI-to-distance relationshipmodels, applying a chi-square test to evaluate a fit between (1) theplurality of RSSI-to-distance data pairs for the plurality of V2Xmessages and (2) the predetermined RSSI-to-distance relationship model,to generate a fit score; and

means for, based on the fit score for each predeterminedRSSI-to-distance relationship model from the plurality of predeterminedRSSI-to-distance relationship models, choosing the selected,predetermined RSSI-to-distance relationship model as a best-fittingmodel.

Clause 40. The system of clause 38 or 39, wherein the plurality ofpredetermined RSSI-to-distance relationship models correspond to aplurality of signal propagation contexts.

41. A non-transitory computer-readable medium storing instructionstherein for execution by one or more processing units, comprisinginstructions to:

at a receiving device, receive a plurality of V2X messages from aplurality of transmitting devices;

obtain a plurality of RSSI-to-distance data pairs, including anRSSI-to-distance data pair for each of the plurality of V2X messages;

establish an RSSI-to-distance relationship model based, at least inpart, on the plurality of RSSI-to-distance data pairs;

at the receiving device, receive an additional V2X message from atransmitting device different from the plurality of transmittingdevices;

obtain an additional RSSI-to-distance data pair for the additional V2Xmessage;

compare the additional RSSI-to-distance data pair to theRSSI-to-distance relationship model; and

in response to determining that the additional RSSI-to-distance datapair fails a criterion for conforming to the RSSI-to-distancerelationship model, identify the additional V2X message as an abnormaltransmission

Clause 42. The non-transitory computer-readable medium of clause 41,wherein the instructions to establish the RSSI-to-distance relationshipmodel comprise instructions to select a predetermined RSSI-to-distancerelationship model from a plurality of predetermined RSSI-to-distancerelationship models.

Clause 43. The non-transitory computer-readable medium of clause 42,wherein the instructions to select the predetermined RSSI-to-distancerelationship model from the plurality of predetermined RSSI-to-distancerelationship models comprise instructions to:

for each predetermined RSSI-to-distance relationship model from theplurality of predetermined RSSI-to-distance relationship models, apply achi-square test to evaluate a fit between (1) the plurality ofRSSI-to-distance data pairs for the plurality of V2X messages and (2)the predetermined RSSI-to-distance relationship model, to generate a fitscore; and

based on the fit score for each predetermined RSSI-to-distancerelationship model from the plurality of predetermined RSSI-to-distancerelationship models, choose the selected, predetermined RSSI-to-distancerelationship model as a best-fitting model.

Clause 44. The non-transitory computer-readable medium of clause 42 or43, wherein the plurality of predetermined RSSI-to-distance relationshipmodels correspond to a plurality of signal propagation contexts.

What is claimed is:
 1. A method for abnormal transmissionidentification, comprising: at a receiving device, receiving a pluralityof V2X messages from a plurality of transmitting devices; obtaining aplurality of RSSI-to-distance data pairs, including an RSSI-to-distancedata pair for each of the plurality of V2X messages, each of theplurality of RSSI-to-distance data pairs comprising (1) a receivedsignal strength indicator (RSSI) value and (2) an estimated distancebetween the receiving device and a corresponding one of the plurality oftransmitting devices; establishing an RSSI-to-distance relationshipmodel based, at least in part, on the plurality of RSSI-to-distance datapairs; at the receiving device, receiving an additional V2X message froma transmitting device different from the plurality of transmittingdevices; obtaining an additional RSSI-to-distance data pair for theadditional V2X message, the additional RSSI-to-distance pair comprising(1) an RSSI value and (2) an estimated distance between the receivingdevice and the transmitting device of the additional V2X messagecomputed from (a) location data contained in the additional V2X messageand (b) a location fix for the receiving device; comparing theadditional RSSI-to-distance data pair to the RSSI-to-distancerelationship model; and in response to determining that the additionalRSSI-to-distance data pair fails a criterion for conforming to theRSSI-to-distance relationship model, identifying the additional V2Xmessage as an abnormal transmission.
 2. The method of claim 1, whereinestablishing the RSSI-to-distance relationship model comprises selectinga predetermined RSSI-to-distance relationship model from a plurality ofpredetermined RSSI-to-distance relationship models.
 3. The method ofclaim 2, wherein the selecting the predetermined RSSI-to-distancerelationship model from the plurality of predetermined RSSI-to-distancerelationship models comprises: for each predetermined RSSI-to-distancerelationship model from the plurality of predetermined RSSI-to-distancerelationship models, applying a chi-square test to evaluate a fitbetween (1) the plurality of RSSI-to-distance data pairs for theplurality of V2X messages and (2) the predetermined RSSI-to-distancerelationship model, to generate a fit score; and based on the fit scorefor each predetermined RSSI-to-distance relationship model from theplurality of predetermined RSSI-to-distance relationship models,choosing the selected, predetermined RSSI-to-distance relationship modelas a best-fitting model.
 4. The method of claim 2, wherein the pluralityof predetermined RSSI-to-distance relationship models correspond to aplurality of signal propagation contexts.
 5. The method of claim 4,wherein the plurality of predetermined RSSI-to-distance relationshipmodels include at least one of: a city simple path loss model, a highwaysimple path loss model, a city two-ray model, or a city breakpointmodel.
 6. The method of claim 2, wherein selection of the predeterminedRSSI-to-distance relationship model is repeatedly performed based on atime schedule.
 7. The method of claim 2, wherein selection of thepredetermined RSSI-to-distance relationship model is repeatedlyperformed based on an event trigger.
 8. The method of claim 1 wherein,for each V2X message of the plurality of V2X messages and the additionalV2X message: the V2X message comprises a Basic Safety Message (BSM),Cooperative Awareness Message (CAM), and/or Decentralized EnvironmentalNotification Message (DENM), the RSSI value is associated with receptionof the BSM/CAM/DENM message, and the estimated distance is based in parton location data contained in the BSM/CAM/DENM message.
 9. The method ofclaim 1, wherein the determining that the additional RSSI-to-distancedata pair fails the criterion for conforming to the RSSI-to-distancerelationship model comprises: establishing, for an estimated distancefor the additional V2X message, an upper RSSI value and a lower RSSIvalue corresponding to the RSSI-to-distance relationship model; anddetermining that an RSSI value for the additional V2X message fallsoutside an RSSI range established by the upper RSSI value and the lowerRSSI value.
 10. The method of claim 1, further comprising: detecting anobject in a vicinity of the transmitting device or the receiving devicebased on readings obtained from one or more sensors, wherein thecriterion for conforming to the RSSI-to-distance relationship model isbased, at least in part, on detecting the object in the vicinity of thetransmitting device or the receiving device.
 11. The method of claim 10,wherein detecting the object in the vicinity of the transmitting deviceor the receiving device comprises determining that the object obstructsa line of sight (LOS) between the transmitting device and the receivingdevice.
 12. The method of claim 10, wherein the criterion for conformingto the RSSI-to-distance relationship model is associated with an RSSIrange, and the RSSI range is expanded or shifted in response todetecting the object in the vicinity of the transmitting device or thereceiving device.
 13. A method for abnormal transmission identification,comprising: at a receiving device, receiving a plurality of V2X messagesfrom a plurality of transmitting devices, wherein the plurality of V2Xmessages correspond to a plurality of RSSI-to-distance data pairs,including an RSSI-to-distance data pair for each of the plurality of V2Xmessages, each of the plurality of RSSI-to-distance data pairscomprising (1) a received signal strength indicator (RSSI) value and (2)an estimated distance between the receiving device and a correspondingone of the plurality of transmitting devices, wherein the plurality ofRSSI-to-distance data pairs correspond to an RSSI-to-distancerelationship model; at the receiving device, receiving an additional V2Xmessage from a transmitting device different from the plurality oftransmitting devices, wherein the additional V2X message corresponds toan additional RSSI-to-distance data pair comprising (1) an RSSI valueand (2) an estimated distance between the receiving device and thetransmitting device of the additional V2X message computed from (a)location data contained in the additional V2X message and (b) a locationfix for the receiving device, and the additional RSSI-to-distance datapair fails a criterion for conforming to the RSSI-to-distancerelationship model; and at the receiving device, identifying theadditional V2X message as an abnormal transmission.
 14. An apparatus forabnormal transmission identification, comprising: a receive radio unitconfigured to receive a plurality of V2X messages from a plurality oftransmitting devices and receive an additional V2X message from atransmitting device different from the plurality of transmittingdevices; and one or more processors configured to: obtain a plurality ofRSSI-to-distance data pairs, including an RSSI-to-distance data pair foreach of the plurality of V2X messages, each of the plurality ofRSSI-to-distance data pairs comprising (1) a received signal strengthindicator (RSSI) value and (2) an estimated distance between thereceiving device and a corresponding one of the plurality oftransmitting devices; establish an RSSI-to-distance relationship modelbased, at least in part, on the plurality of RSSI-to-distance datapairs; obtain an additional RSSI-to-distance data pair for theadditional V2X message, the additional RSSI-to-distance pair comprising(1) an RSSI value and (2) an estimated distance between the receivingdevice and the transmitting device of the additional V2X messagecomputed from (a) location data contained in the additional V2X messageand (b) a location fix for the receiving device; compare the additionalRSSI-to-distance data pair to the RSSI-to-distance relationship model;and in response to determining that the additional RSSI-to-distance datapair fails a criterion for conforming to the RSSI-to-distancerelationship model, identify the additional V2X message as an abnormaltransmission.
 15. The apparatus of claim 1, wherein the one or moreprocessors are configured to establish the RSSI-to-distance relationshipmodel by selecting a predetermined RSSI-to-distance relationship modelfrom a plurality of predetermined RSSI-to-distance relationship models.16. The apparatus of claim 15, wherein the one or more processors areconfigured to select the predetermined RSSI-to-distance relationshipmodel from the plurality of predetermined RSSI-to-distance relationshipmodels by: for each predetermined RSSI-to-distance relationship modelfrom the plurality of predetermined RSSI-to-distance relationshipmodels, applying a chi-square test to evaluate a fit between (1) theplurality of RSSI-to-distance data pairs for the plurality of V2Xmessages and (2) the predetermined RSSI-to-distance relationship model,to generate a fit score; and based on the fit score for eachpredetermined RSSI-to-distance relationship model from the plurality ofpredetermined RSSI-to-distance relationship models, choosing theselected, predetermined RSSI-to-distance relationship model as abest-fitting model.
 17. The apparatus of claim 15, wherein the pluralityof predetermined RSSI-to-distance relationship models correspond to aplurality of signal propagation contexts.
 18. The apparatus of claim 17,wherein the plurality of predetermined RSSI-to-distance relationshipmodels include at least one of: a city simple path loss model, a highwaysimple path loss model, a city two-ray model, or a city breakpointmodel.
 19. The apparatus of claim 15, wherein the one or more processorsare configured to repeated select the predetermined RSSI-to-distancerelationship model, based on a time schedule.
 20. The apparatus of claim15, wherein the one or more processors are configured to repeated selectthe predetermined RSSI-to-distance relationship model, based on an eventtrigger.
 21. The apparatus of claim 14 wherein, for each V2X message ofthe plurality of V2X messages and the additional V2X message: the V2Xmessage comprises a Basic Safety Message (BSM), Cooperative AwarenessMessage (CAM), and/or Decentralized Environmental Notification Message(DENM), the RSSI value is associated with reception of the BSM/CAM/DENMmessage, and the estimated distance is based in part on location datacontained in the BSM/CAM/DENM message.
 22. The apparatus of claim 14,wherein the one or more processors are configured to determine that theadditional RSSI-to-distance data pair fails the criterion for conformingto the RSSI-to-distance relationship, by: establishing, for an estimateddistance for the additional V2X message, an upper RSSI value and a lowerRSSI value corresponding to the RSSI-to-distance relationship model; anddetermining that an RSSI value for the additional V2X message fallsoutside an RSSI range established by the upper RSSI value and the lowerRSSI value.
 23. The apparatus of claim 14, further comprising: detectingan object in a vicinity of the transmitting device or the apparatusbased on readings obtained from one or more sensors, wherein thecriterion for conforming to the RSSI-to-distance relationship model isbased, at least in part, on detecting the object in the vicinity of thetransmitting device or the apparatus.
 24. The apparatus of claim 23,wherein detecting the object in the vicinity of the transmitting deviceor the apparatus comprises determining that the object obstructs a lineof sight (LOS) between the transmitting device and the apparatus. 25.The apparatus of claim 23, wherein the criterion for conforming to theRSSI-to-distance relationship model is associated with an RSSI range,and the RSSI range is expanded or shifted in response to detecting theobject in the vicinity of the transmitting device or the apparatus. 26.A system for abnormal transmission identification, comprising: meansfor, at a receiving device, receiving a plurality of V2X messages from aplurality of transmitting devices; means for obtaining a plurality ofRSSI-to-distance data pairs, including an RSSI-to-distance data pair foreach of the plurality of V2X messages, each of the plurality ofRSSI-to-distance data pairs comprising (1) a received signal strengthindicator (RSSI) value and (2) an estimated distance between thereceiving device and a corresponding one of the plurality oftransmitting devices; means for establishing an RSSI-to-distancerelationship model based, at least in part, on the plurality ofRSSI-to-distance data pairs; means for, at the receiving device,receiving an additional V2X message from a transmitting device differentfrom the plurality of transmitting devices; means for obtaining anadditional RSSI-to-distance data pair for the additional V2X message,the additional RSSI-to-distance pair comprising (1) an RSSI value and(2) an estimated distance between the receiving device and thetransmitting device of the additional V2X message computed from (a)location data contained in the additional V2X message and (b) a locationfix for the receiving device; means for comparing the additionalRSSI-to-distance data pair to the RSSI-to-distance relationship model;and means for, in response to determining that the additionalRSSI-to-distance data pair fails a criterion for conforming to theRSSI-to-distance relationship model, identifying the additional V2Xmessage as an abnormal transmission.